Statistics chart row mode drill down

ABSTRACT

In embodiments of statistics chart row mode drill down, a first interface is displayed in a table format that includes columns and rows, where each row is associated with an event and each column includes field for a respective event. The rows can further include one or more aggregated metrics representing a number of events associated with a respective row. A row can be emphasized in the first interface and, in response a menu can be displayed with selectable options to transition to a second interface, where the data displayed by the second interface is based on an option selected from the menu.

RELATED APPLICATIONS

This application is a continuation U.S. patent application Ser. No.16/595,819, filed Oct. 8, 2019, which is a continuation-in-part of U.S.patent application Ser. No. 15/885,486, filed Jan. 31, 2018, and titled“Row Drill Down of an Event Statistics Time Chart,” which itself is acontinuation of U.S. patent application Ser. No. 14/526,454, filed onOct. 28, 2014, and titled “Statistics Time Chart Interface Row ModeDrill Down.” The entire contents of each of the aforementionedapplications are hereby incorporated by reference herein in theirentirety.

U.S. patent application Ser. No. 14/526,454 itself claims the benefit ofU.S. Provisional Patent Application Ser. No. 62/059,988, filed Oct. 5,2014, titled “Event Segment Search Drill Down;” U.S. Provisional PatentApplication Ser. No. 62/059,989, filed Oct. 5, 2014, titled “Field ValueSearch Drill Down;” U.S. Provisional Patent Application Ser. No.62/059,993, filed Oct. 5, 2014 and U.S. Provisional Patent ApplicationSer. No. 62/060,545, filed Oct. 6, 2014, both titled “Statistics ValueChart Interface Row Mode Drill Down;” U.S. Provisional PatentApplication Ser. No. 62/059,994, filed Oct. 5, 2014 and U.S. ProvisionalPatent Application Ser. No. 62/060,551, filed Oct. 6, 2014, both titled“Statistics Time Chart Interface Row Mode Drill Down;” U.S. ProvisionalPatent Application Ser. No. 62/059,998, filed Oct. 5, 2014 and U.S.Provisional Patent Application Ser. No. 62/060,560, filed Oct. 6, 2014,both titled “Statistics Value Chart Interface Cell Mode Drill Down;” andU.S. Provisional Patent Application Ser. No. 62/060,001, filed Oct. 5,2014 and U.S. Provisional Patent Application Ser. No. 62/060,567, filedOct. 6, 2014, both titled “Statistics Time Chart Interface Cell ModeDrill Down.” The entire contents of each of the aforementionedapplications are hereby incorporated by reference herein in theirentirety

BACKGROUND

Data analysts for many businesses face the challenge of making sense ofand finding patterns in the increasingly large amounts of data in themany types and formats that such businesses generate and collect. Forexample, accessing computer networks and transmitting electroniccommunications across the networks generates massive amounts of data,including such types of data as machine data and Web logs. Identifyingpatterns in this data, once thought relatively useless, has proven to beof great value to the businesses. In some instances, pattern analysiscan indicate which patterns are normal and which ones are unusual. Forexample, detecting unusual patterns can allow a computer system managerto investigate the circumstances and determine whether a computer systemsecurity threat exists.

Additionally, analysis of the data allows businesses to understand howtheir employees, potential consumers, and/or Web visitors use thecompany's online resources. Such analysis can provide businesses withoperational intelligence, business intelligence, and an ability tobetter manage their IT resources. For instance, such analysis may enablea business to better retain customers, meet customer needs, or improvethe efficiency of the company's IT resources. Despite the value that onecan derive from the underlying data described, making sense of this datato realize that value takes effort. In particular, patterns inunderlying data may be difficult to identify or understand whenanalyzing specific behaviors in isolation, often resulting in thefailure of a data analyst to notice valuable correlations in the datafrom which a business can draw strategic insight.

SUMMARY

This Summary introduces features and concepts of statistics chart rowmode drill down, which is further described below in the DetailedDescription and/or shown in the Figures. In various examples, thestatistic chart can be a statistics value chart interface and/or astatistics time chart interface. This Summary should not be consideredto describe essential features of the claimed subject matter, nor usedto determine or limit the scope of the claimed subject matter.

In various examples, a statistics value chart interface row mode drilldown is described. In embodiments, a search system exposes a statisticsvalue chart interface for display in a table format that includescolumns each with field values of an event field, and each column havinga column heading of a different one of the event fields, and includesrows each with one or more of the field values, where each field valuein a row is associated with a different one of the event fields, andeach row includes an aggregated metric that represents a number ofevents having field-value pairs that match all of the one or more fieldvalues listed in a respective row and the corresponding event fieldslisted in the respective columns. A row of the field values and thecorresponding aggregated metric can be emphasized in the firstinterface, and in response, a menu is displayed with options that areselectable. The menu includes the options to transition to a searchevents interface that displays either a listing of the events thatinclude the field-value pairs that match all of the field values listedin the emphasized row, or other events that do not include thefield-value pairs that match all of the field values listed in theemphasized row.

In various examples, a statistics time chart interface row mode drilldown is described. In embodiments, a search system exposes a statisticstime chart interface for display in a table format that includes columnseach having a column heading comprising a different value, eachdifferent value associated with a particular event field, and includesrows each with a time increment and one or more aggregated metrics, eachaggregated metric representing a number of events having a field-valuepair that matches the different value represented in one of the columnsand within the time increment over which the aggregated metric iscalculated. A row that includes the time increment and the aggregatedmetrics can be emphasized in the first interface, and in response, amenu is displayed with options that are selectable. The menu includesthe options to transition to a search events interface, or transition toa statistics narrowed time interface for the time increment in theemphasized row. The menu also includes a designation of a time durationthat encompasses the time increment corresponding to the emphasized row.

In various examples, a selection of the aggregated metric in anemphasized row initiates the display of the menu with the options thatinclude a view events option and an other events option. An inputassociated with the emphasized row can be received, such as wheninitiated by a user in the statistics value chart interface, and themenu of the options is displayed proximate the emphasized row in thestatistics value chart interface. For example, the menu may pop-up ordrop-down just below the emphasized row. The view events option isselectable to transition to the search events interface that displaysthe listing of the events that include the field-value pairs that matchall of the field values listed in the emphasized row. The other eventsoption is also selectable to transition to the search events interfacethat displays the listing of the other events that do not include thefield-value pairs that match all of the field values listed in theemphasized row. The menu also includes a designation of a field-valuepair associated with the emphasized row.

In various examples, a selection of an emphasized row initiates thedisplay of the menu with the options that include a view events optionand a narrow time range option. An input associated with the emphasizedrow can be received, such as when initiated by a user in the statisticstime chart interface, and the menu of the options is displayed proximatethe emphasized row in the statistics time chart interface. For example,the menu may pop-up or drop-down just below the emphasized row. The viewevents option is selectable to transition to the search events interfacethat displays a list of the events that include the field-value pairthat matches the different value represented in one of the columns andwithin the time increment of the emphasized row. The narrow time rangeoption is selectable to said transition to the statistics narrowed timeinterface, which includes the columns each having the column headingcomprising a different value associated with the particular event fieldas displayed in the statistics time chart interface. The statisticsnarrowed time interface also includes time metric rows, each with anarrowed time metric of the time increment of the emphasized row in thestatistics time chart interface, where each of the time metric rowsfurther include additional aggregated metrics, and each additionalaggregated metric identifies a number of events having the field-valuepair that matches the different value represented in one of the columnsand within the narrowed time metric.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of statistics chart row mode drill down are described withreference to the following Figures. The same numbers may be usedthroughout to reference like features and components that are shown inthe Figures:

FIG. 1 illustrates a block diagram of an event-processing system inaccordance with the disclosed implementations of statistics chart rowmode drill down.

FIG. 2 illustrates a flowchart of how indexers process, index, and storedata received from forwarders in accordance with the disclosedimplementations.

FIG. 3 illustrates a flowchart of how a search head and indexers performa search query in accordance with the disclosed implementations.

FIG. 4 illustrates a block diagram of a system for processing searchrequests that uses extraction rules for field values in accordance withthe disclosed implementations.

FIG. 5 illustrates an exemplary search query received from a client andexecuted by search peers in accordance with the disclosedimplementations.

FIG. 6A illustrates a search screen in accordance with the disclosedimplementations.

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed implementations.

FIG. 7A illustrates a key indicators view in accordance with thedisclosed implementations.

FIG. 7B illustrates an incident review dashboard in accordance with thedisclosed implementations.

FIG. 7C illustrates a proactive monitoring tree in accordance with thedisclosed implementations.

FIG. 7D illustrates a screen displaying both log data and performancedata in accordance with the disclosed implementations.

FIGS. 8A-8C illustrate examples of statistics search interfaces in rowmode in accordance with the disclosed implementations.

FIG. 9A-9C illustrate examples of statistics search interfaces in rowmode in accordance with the disclosed implementations.

FIG. 10 illustrates example method(s) of statistics value chartinterface row mode drill down in accordance with one or moreembodiments.

FIG. 11 illustrates example method(s) of statistics time chart interfacerow mode drill down in accordance with one or more embodiments.

FIG. 12 illustrates an example system with an example device that canimplement embodiments of statistics chart row mode drill down.

DETAILED DESCRIPTION

Embodiments of statistics interface row mode search drill down aredescribed and can be implemented to facilitate user-initiated searchoptions when performing data searches in statistics value chartinterfaces and in statistics time chart interfaces. A statistics valuechart interface includes columns each with field values of an eventfield, and each column having a column heading of a different one of theevent fields, and includes rows each with one or more of the fieldvalues, where each field value in a row is associated with a differentone of the event fields, and each row includes an aggregated metric thatrepresents a number of events having field-value pairs that match all ofthe one or more field values listed in a respective row and thecorresponding event fields listed in the respective columns. A row ofthe field values and the corresponding aggregated metric can beemphasized in the statistics value chart interface, and in response, amenu is displayed with event options that are selectable. The menuincludes the options to transition to a search events interface thatdisplays either a listing of the events that include the field-valuepairs that match all of the field values listed in the emphasized row,or other events that do not include the field-value pairs that match allof the field values listed in the emphasized row.

Additionally, a statistics time chart interface includes columns eachhaving a column heading comprising a different value, each differentvalue associated with a particular event field, and includes rows eachwith a time increment and one or more aggregated metrics, eachaggregated metric representing a number of events having a field-valuepair that matches the different value represented in one of the columnsand within the time increment over which the aggregated metric iscalculated. A row that includes the time increment and the aggregatedmetrics can be emphasized in the statistics time chart interface, and inresponse, a menu is displayed with options that are selectable. The menuincludes the options to transition to a search events interface, ortransition to a statistics narrowed time interface for the timeincrement in the emphasized row.

Example Environment

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that were selected at ingestiontime.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., to store and process performance data. The SPLUNK®ENTERPRISE system is the leading platform for providing real-timeoperational intelligence that enables organizations to collect, index,and harness machine-generated data from various websites, applications,servers, networks, and mobile devices that power their businesses. TheSPLUNK® ENTERPRISE system is particularly useful for analyzingunstructured performance data, which is commonly found in system logfiles. Although many of the techniques described herein are explainedwith reference to the SPLUNK® ENTERPRISE system, the techniques are alsoapplicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” in which each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” in which time series data includes a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, inwhich specific data items with specific data formats reside atpredefined locations in the data. For example, structured data caninclude data items stored in fields in a database table. In contrast,unstructured data does not have a predefined format. This means thatunstructured data can include various data items having different datatypes that can reside at different locations. For example, when the datasource is an operating system log, an event can include one or morelines from the operating system log containing raw data that includesdifferent types of performance and diagnostic information associatedwith a specific point in time.

Examples of data sources from which an event may be derived include, butare not limited to web servers, application servers, databases,firewalls, routers, operating systems, and software applications thatexecute on computer systems, mobile devices, and sensors. The datagenerated by such data sources can be produced in various formsincluding, for example and without limitation, server log files,activity log files, configuration files, messages, network packet data,performance measurements and sensor measurements. An event typicallyincludes a timestamp that may be derived from the raw data in the event,or may be determined through interpolation between temporally proximateevents having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, in which theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly” as desired(e.g., at search time), rather than at ingestion time of the data as intraditional database systems. Because the schema is not applied to eventdata until it is desired (e.g., at search time), it is referred to as a“late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the timestamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction ruleincludes a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques. Also, a number of “defaultfields” that specify metadata about the events, rather than data in theevents themselves, can be created automatically. For example, suchdefault fields can specify: a timestamp for the event data; a host fromwhich the event data originated; a source of the event data; and asource type for the event data. These default fields may be determinedautomatically when the events are created, indexed, or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

Data Server System

FIG. 1 illustrates a block diagram of an example event-processing system100, similar to the SPLUNK® ENTERPRISE system, and in which embodimentsof statistics chart row mode drill down can be implemented. In variousexamples, the statistics chart can be a statistics value chart interfaceand/or a statistics time chart interface. The example event-processingsystem 100 includes one or more forwarders 101 that collect dataobtained from a variety of different data sources 105, and one or moreindexers 102 that store, process, and/or perform operations on thisdata, in which each indexer operates on data contained in a specificdata store 103. A search head 104 may also be provided that representsfunctionality to obtain and process search requests from clients andprovide results of the search back to the clients, additional details ofwhich are discussed in relation to FIGS. 3 and 4 . The forwarders 101,indexers 102, and/or search head 104 may be configured as separatecomputer systems in a data center, or alternatively may be configured asseparate processes implemented via one or more individual computersystems. Data that is collected via the forwarders 101 may be obtainedfrom a variety of different data sources 105.

As further illustrated, the search head 104 may interact with a clientapplication module 106 associated with a client device, such as toobtain search queries and supply search results or other suitable databack to the client application module 106 that is effective to enablethe client application module 106 to form search user interfaces 108through which different views of the data may be exposed. Variousexamples and details regarding search interfaces 108, client applicationmodules 106, search queries, and operation of the various componentsillustrated in FIG. 1 are discussed throughout this document.

During operation, the forwarders 101 identify which indexers 102 willreceive the collected data and then forward the data to the identifiedindexers. The forwarders 101 can also perform operations to strip outextraneous data and detect timestamps in the data. The forwarders nextdetermine which of the indexers 102 will receive each data item and thenforward the data items to the determined indexers 102. Note thatdistributing data across the different indexers 102 facilitates parallelprocessing. This parallel processing can take place at data ingestiontime, because multiple indexers can process the incoming data inparallel. The parallel processing can also take place at search time,because multiple indexers can search through the data in parallel.

The example event-processing system 100 and the processes describedbelow with respect to FIGS. 1-5 are further described in “ExploringSplunk Search Processing Language (SPL) Primer and Cookbook” by DavidCarasso, CITO Research, 2012, and in “Optimizing Data Analysis With aSemi-Structured Time Series Database” by Ledion Bitincka, ArchanaGanapathi, Stephen Sorkin, and Steve Zhang, SLAML, 2010, each of whichis hereby incorporated herein by reference in its entirety for allpurposes.

Data Ingestion

FIG. 2 illustrates a flowchart 200 of how an indexer processes, indexes,and stores data received from forwarders in accordance with thedisclosed embodiments. At block 201, the indexer receives the data fromthe forwarder. Next, at block 202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, in which the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, in which the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. Asmentioned above, these timestamps can be determined by extracting thetime directly from data in the event, or by interpolating the time basedon timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 204, for example by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in block 206. Then, at block 207 theindexer includes the identified keywords in an index, which associateseach stored keyword with references to events containing that keyword(or to locations within events where that keyword is located). When anindexer subsequently receives a keyword-based query, the indexer canaccess the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign orcolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2” as a field-value pair.

Finally, the indexer stores the events in a data store at block 208,where a timestamp can be stored with each event to facilitate searchingfor events based on a time range. In some cases, the stored events areorganized into a plurality of buckets, where each bucket stores eventsassociated with a specific time range. This not only improves time-basedsearches, but it also allows events with recent timestamps that may havea higher likelihood of being accessed to be stored in faster memory tofacilitate faster retrieval. For example, a bucket containing the mostrecent events can be stored as flash memory instead of on a hard disk.

Each indexer 102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example using map-reduce techniques,in which each indexer returns partial responses for a subset of eventsto a search head that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, an indexermay further optimize searching by looking only in buckets for timeranges that are relevant to a query. Moreover, events and buckets canalso be replicated across different indexers and data stores tofacilitate high availability and disaster recovery as is described inU.S. patent application Ser. No. 14/266,812 filed on 30 Apr. 2014, andin U.S. patent application Ser. No. 14/266,817 also filed on 30 Apr.2014.

Query Processing

FIG. 3 illustrates a flowchart 300 of how a search head and indexersperform a search query in accordance with the disclosed embodiments. Atthe start of this process, a search head receives a search query from aclient (e.g., a client computing device) at block 301. Next, at block302, the search head analyzes the search query to determine whatportions can be delegated to indexers and what portions need to beexecuted locally by the search head. At block 303, the search headdistributes the determined portions of the query to the indexers. Notethat commands that operate on single events can be trivially delegatedto the indexers, while commands that involve events from multipleindexers are harder to delegate.

Then, at block 304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 304 may involve using the late-binding scheme to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending on what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by system 100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these parameters to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

Field Extraction

FIG. 4 illustrates a block diagram 400 of how fields can be extractedduring query processing in accordance with the disclosed embodiments. Atthe start of this process, a search query 402 is received at a queryprocessor 404. The query processor 404 includes various mechanisms forprocessing a query, where these mechanisms can reside in a search head104 and/or an indexer 102. Note that the exemplary search query 402illustrated in FIG. 4 is expressed in Search Processing Language (SPL),which is used in conjunction with the SPLUNK® ENTERPRISE system. The SPLis a pipelined search language in which a set of inputs is operated onby a first command in a command line, and then a subsequent commandfollowing the pipe symbol “|” operates on the results produced by thefirst command, and so on for additional commands. Search query 402 canalso be expressed in other query languages, such as the Structured QueryLanguage (“SQL”) or any suitable query language.

Upon receiving the search query 402, the query processor 404 identifiesthat the search query 402 includes two fields, “IP” and “target.” Thequery processor 404 also determines that the values for the “IP” and“target” fields have not already been extracted from events in a datastore 414, and consequently determines that the query processor 404needs to use extraction rules to extract values for the fields. Hence,the query processor 404 performs a lookup for the extraction rules in arule base 406, in which rule base 406 maps field names to correspondingextraction rules and obtains extraction rules 408 and 409, whereextraction rule 408 specifies how to extract a value for the “IP” fieldfrom an event, and extraction rule 409 specifies how to extract a valuefor the “target” field from an event.

As is illustrated in FIG. 4 , the extraction rules 408 and 409 caninclude regular expressions that specify how to extract values for therelevant fields. Such regular-expression-based extraction rules are alsoreferred to as “regex rules.” In addition to specifying how to extractfield values, the extraction rules may also include instructions forderiving a field value by performing a function on a character string orvalue retrieved by the extraction rule. For example, a transformationrule may truncate a character string, or convert the character stringinto a different data format. In some cases, the query itself canspecify one or more extraction rules.

Next, the query processor 404 sends the extraction rules 408 and 409 toa field extractor 412, which applies the extraction rules 408 and 409 toevents 416-418 in the data store 414. Note that the data store 414 caninclude one or more data stores, and the extraction rules 408 and 409can be applied to large numbers of events in the data store 414, and arenot meant to be limited to the three events 416-418 illustrated in FIG.4 . Moreover, the query processor 404 can instruct the field extractor412 to apply the extraction rules to all of the events in the data store414, or to a subset of the events that have been filtered based on somecriteria.

Next, the field extractor 412 applies the extraction rule 408 for thefirst command “Search IP=“10*” to events in the data store 414,including the events 416-418. The extraction rule 408 is used to extractvalues for the IP address field from events in the data store 414 bylooking for a pattern of one or more digits, followed by a period,followed again by one or more digits, followed by another period,followed again by one or more digits, followed by another period, andfollowed again by one or more digits. Next, the field extractor 412returns field values 420 to the query processor 404, which uses thecriterion IP=“10*” to look for IP addresses that start with “10”. Notethat events 416 and 417 match this criterion, but event 418 does not, sothe result set for the first command is events 416 and 417.

The query processor 404 then sends the events 416 and 417 to the nextcommand “stats count target.” To process this command, the queryprocessor 404 causes the field extractor 412 to apply the extractionrule 409 to the events 416 and 417. The extraction rule 409 is used toextract values for the target field for the events 416 and 417 byskipping the first four commas in the events, and then extracting all ofthe following characters until a comma or period is reached. Next, thefield extractor 412 returns field values 421 to the query processor 404,which executes the command “stats count target” to count the number ofunique values contained in the target fields, which in this exampleproduces the value “2” that is returned as a final result 422 for thequery.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, the queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or chart, generated from the values.

Example Search Screen

FIG. 6A illustrates an example of a search screen 600 in accordance withthe disclosed embodiments. The search screen 600 includes a search bar602 that accepts user input in the form of a search string. It alsoincludes a date time range picker 612 that enables the user to specify adate and/or time range for the search. For “historical searches” theuser can select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday,” or “last week.” For “real-timesearches,” the user can select the size of a preceding time window tosearch for real-time events. The search screen 600 also initiallydisplays a “data summary” dialog 610 as is illustrated in FIG. 6B thatenables the user to select different sources for the event data, such asby selecting specific hosts and log files.

After the search is executed, the search screen 600 can display theresults through search results tabs 604, where the search results tabs604 include: an “Events” tab that displays various information aboutevents returned by the search; a “Patterns” tab that can be selected todisplay various patterns about the events returned by the search; a“Statistics” tab that displays statistics about the search results andevents; and a “Visualization” tab that displays various visualizationsof the search results. The “Events” tab illustrated in FIG. 6A displaysa timeline graph 605 that graphically illustrates the number of eventsthat occurred in one-hour intervals over the selected time range. Italso displays an events list 608 that enables a user to view the rawdata in each of the returned events. It additionally displays a fieldssidebar 606 that includes statistics about occurrences of specificfields in the returned events, including “selected fields” that arepre-selected by the user, and “interesting fields” that areautomatically selected by the system based on pre-specified criteria.

Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

Map-Reduce Technique

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 5 illustrates an example500 of how a search query 501 received from a client at search head 104can split into two phases, including: (1) a “map phase” comprisingsubtasks 502 (e.g., data retrieval or simple filtering) that may beperformed in parallel and are “mapped” to indexers 102 for execution,and (2) a “reduce phase” comprising a merging operation 503 to beexecuted by the search head 104 when the results are ultimatelycollected from the indexers.

During operation, upon receiving search query 501, search head 104modifies search query 501 by substituting “stats” with “prestats” toproduce search query 502, and then distributes search query 502 to oneor more distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 3 , or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

Keyword Index

As described above with reference to the flow charts 200 and 300 shownin respective FIGS. 2 and 3 , the event-processing system 100 canconstruct and maintain one or more keyword indices to facilitate rapidlyidentifying events containing specific keywords. This can greatly speedup the processing of queries involving specific keywords. As mentionedabove, to build a keyword index, an indexer first identifies a set ofkeywords. Then, the indexer includes the identified keywords in anindex, which associates each stored keyword with references to eventscontaining that keyword, or to locations within events where thatkeyword is located. When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword.

High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 100make use of a high-performance analytics store, which is referred to asa “summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an entry in a summarization table can keep track of occurrences of thevalue “94107” in a “ZIP code” field of a set of events, where the entryincludes references to all of the events that contain the value “94107”in the ZIP code field. This enables the system to quickly processqueries that seek to determine how many events have a particular valuefor a particular field, because the system can examine the entry in thesummarization table to count instances of the specific value in thefield without having to go through the individual events or doextractions at search time. Also, if the system needs to process each ofthe events that have a specific field-value combination, the system canuse the references in the summarization table entry to directly accessthe events to extract further information without having to search eachof the events to find the specific field-value combination at searchtime.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range, where a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, in which theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover each of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example where a value in the report depends onrelationships between events from different time periods. If reports canbe accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only the events withinthe time period that meet the specified criteria. Similarly, if thequery seeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that match thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated.

Alternatively, if the system stores events in buckets covering specifictime ranges, then the summaries can be generated on a bucket-by-bucketbasis. Note that producing intermediate summaries can save the workinvolved in re-running the query for previous time periods, so only thenewer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, issued on Nov. 19, 2013, and in U.S.Pat. No. 8,412,696, issued on Apr. 2, 2011.

Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards,and visualizations that make it easy for developers to createapplications to provide additional capabilities. One such application isthe SPLUNK® APP FOR ENTERPRISE SECURITY, which performs monitoring andalerting operations, and includes analytics to facilitate identifyingboth known and unknown security threats based on large volumes of datastored by the SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional SIEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, where the extracted datais typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. patentapplication Ser. Nos. 13/956,252, and 13/956,262. Security-relatedinformation can also include endpoint information, such as malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices, and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 7A illustratesan exemplary key indicators view 700 that comprises a dashboard, whichcan display a value 701, for various security-related metrics, such asmalware infections 702. It can also display a change in a metric value703, which indicates that the number of malware infections increased bysixty-three (63) during the preceding interval. The key indicators view700 additionally displays a histogram panel 704 that displays ahistogram of notable events organized by urgency values, and a histogrampanel 705 of notable events organized by time intervals. This keyindicators view is described in further detail in pending U.S. patentapplication Ser. No. 13/956,338 filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 7B illustrates an example of an incident review dashboard 710 thatincludes a set of incident attribute fields 711 that, for example,enables a user to specify a time range field 712 for the displayedevents. It also includes a timeline 713 that graphically illustrates thenumber of incidents that occurred in one-hour time intervals over theselected time range. It additionally displays an events list 714 thatenables a user to view a list of each of the notable events that matchthe criteria in the incident attributes fields 711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, or critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent. The incident review dashboard is described further on-line (e.g.,at an HTTP:// site),“docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard.”

Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example by extractingpre-specified data items from the performance data and storing them in adatabase to facilitate subsequent retrieval and analysis at search time.However, the rest of the performance data is not saved and isessentially discarded during pre-processing. In contrast, the SPLUNK®APP FOR VMWARE® stores large volumes of minimally processed performanceinformation and log data at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. patent Ser. No.14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein byreference. Also, see “vSphere Monitoring and Performance,” Update 1,vSphere 5.5, EN-001357-00 on-line (e.g., at an HTTP:// site),“pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.”

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas. The SPLUNK® APP FOR VMWARE® additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Exemplary node-expansionoperations are illustrated in FIG. 7C, where nodes 733 and 734 areselectively expanded. Note that the nodes 731-739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state, or anunknown/off-line state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. patent application Ser. No. 14/235,490 filed on15 Apr. 2014, which is hereby incorporated herein by reference for allpossible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data, and associated performance metrics, for theselected time range. For example, the interface screen illustrated inFIG. 7D displays a listing of recent “tasks and events” and a listing ofrecent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316 filed on 29 Jan. 2014, which is hereby incorporatedherein by reference for all possible purposes.

Statistics Value Chart Interface Row Mode Drill Down

FIG. 8A illustrates an example of a statistics value chart interface 800displayed as a graphical user interface in accordance with the disclosedembodiments for statistics value chart interface row mode drill down.The statistics value chart interface 800 includes a search bar 802 thatdisplays a search command 804. The statistics value chart interface 800displays rows 806 of field values 808 for designated event fields 810.For example, the first row 812 includes the field value“/Users/cburke/Desktop . . . /etc/splunk.version” of the event field“source”, and includes the field value “splunk_version” of the eventfield “source_type”.

The statistics value chart interface 800 also includes aggregatedmetrics 814 that each identify the number of events having the fieldvalues 808 listed in a respective row 806 for corresponding designatedevent fields 810. For example, the first row 812 of the statistics valuechart interface 800 has a corresponding aggregated metric of “1”,indicating that one event includes both of the field-value pairs for“source=/Users/cburke/Desktop . . . /etc/splunk.version” and“source_type=splunk_version”. In implementations, the aggregated metrics814 may represent any type of metric, such as a count, an average, or asum of events, or any other aggregating metric associated with a searchresult set of events. Alternatively or in addition, the aggregatedmetrics 814 may represent an average number of bytes downloaded, a sumof sales, or any other aggregated metric.

In implementations, a row 806 in the statistics value chart interface800 may be highlighted or otherwise emphasized when a pointer that isdisplayed moves over a particular row. This feature is also referred toas highlight with rollover (e.g., detected when a pointer moves over arow). For example, a user may move a computer mouse, stylus, or otherinput device pointer over a row 816, which is then displayed as anemphasized row. The emphasized row can then be selected in response to auser input, such as with a mouse click or touch input to select aparticular row, such as shown and described with reference to FIG. 8B.

The statistics value chart interface 800 can be displayed in a tableformat that includes one or more columns, each column comprising fieldvalues of an event field, and each column having a column headingcomprising a different one of the event fields. The statistics valuechart interface 800 also includes one or more rows, each row comprisingone or more of the field values, each field value in a row associatedwith a different one of the event fields, and each row comprising anaggregated metric that represents a number of events having field-valuepairs that match all of the one or more field values listed in arespective row and the corresponding event fields listed in therespective columns. A row of the one or more field values and thecorresponding aggregated metric can be emphasized in the firstinterface, and in response, a menu is displayed with options that areselectable to transition to a second interface that displays a listingof the events based on a selected one of the options. In embodiments,the options are selectable to transition to the second interface thatdisplays the listing of either the events that include the field-valuepairs that match all of the one or more field values listed in theemphasized row, or other events that do not include the field-valuepairs that match all of the one or more field values listed in theemphasized row.

FIG. 8B further illustrates the example of the statistics value chartinterface 800 described with reference to FIG. 8A in accordance with thedisclosed embodiments for statistics value chart interface row modedrill down. In this example display of the statistics value chartinterface 800, the row 816 is emphasized and a user has selected theemphasized row, such as with a mouse click or touch input, whichinitiates display of a stats event menu 818 that is displayed responsiveto the user input. In implementations, the stats event menu 818 isdisplayed proximate the emphasized row 816 in the statistics value chartinterface 800, such as a pop-up or drop-down menu just below theemphasized row.

The stats event menu 818 includes event options 820 that are selectableto transition to a search events interface that is shown and furtherdescribed with reference to FIG. 8C. A user can select an event option820 from the stats event menu 818 to drill down into events that matchevery single token in an emphasized row, where the “tokens” are thefield values). The search events interface displays either a listing ofthe events that include the field values 808 listed in the emphasizedrow 816, or the search events interface displays other events that donot include the field values 808 listed in the emphasized row. Forexample, the event options 820 displayed in the stats event menu 818include an option “View events” 822 that a user can select to transitionto the search events interface that displays the listing of the eventsthat include the field values 808 listed in the emphasized row 816. Theevent options 820 displayed in the stats event menu 818 also include anoption “Other events” 824 that a user can select to transition to thesearch events interface that displays the listing of the other eventsthat do not include the field values 808 listed in the emphasized row816. The stats event menu 818 also includes a designation 826 of afield-value pair that is associated with the emphasized row in thestatistics value chart interface. In implementations, the designation826 indicates the one or more field-value pairs in the emphasized row.

FIG. 8C illustrates an example of a search events interface 828displayed as a graphical user interface in accordance with the disclosedimplementations. The search events interface 828 includes the search bar802 that displays a search command 804, which is“sourcetype=access_combined” in this example. The search eventsinterface 828 also displays events 830 that are each correlated by adate and time 832. As described previously, the events 830 are a resultset of performing the search command 804 that is currently displayed inthe search bar 802, and only a subset of the events are shown in thesearch events interface. A user can scroll through the list of events830 in the search events interface 828 to view additional events of thesearch result set that are not displayed.

An event 834 (e.g., the first displayed event in the list of events 830)generally incudes displayed event information, depending on a selectedevent view from which a user can select a format to display some or allof the event information for each of the events 830 in the search eventsinterface. In the example search events interface 828, the events 830are displayed in a list view, in which case the displayed eventinformation for event 834 includes event raw data 836 displayed in anupper portion of the event display area, and includes field-value pairs838 displayed in a lower portion of the event display area. In thisexample, each of the events 830 include the current search command 804(e.g., “sourcetype=access_combined”) as a field-value pair 838. Thesearch events interface 828 also includes a fields sidebar 840, whichdisplays the selected fields 842 that are also displayed as the fields838 for each of the events 830, and the fields sidebar 840 includesother interesting fields 844.

Statistics Time Chart Interface Row Mode Drill Down

FIG. 9A illustrates an example of a statistics time chart interface 900displayed as a graphical user interface in accordance with the disclosedembodiments for statistics time chart interface row mode drill down. Thestatistics time chart interface 900 includes a search bar 902 thatdisplays a search command 904. The statistics time chart interface 900displays rows 906, where each row is designated by a time increment 908,and each time increment may include a date associated with the timeincrement. The statistics time chart interface 900 also includes columnsof values 910 that are associated with an event field, such as the field“source” in the search command 904 in this example. Each row in theinterface 900 includes a time increment 908 and one or more aggregatedmetrics 912, where each aggregated metric represents a number of eventshaving the respective value 910 that is listed in the correspondingcolumn and within the time increment.

For example, a row 914 in the statistics time chart interface 900 has atime increment 916 of “2014-09-21 19:00:00”, and includes an aggregatedmetric “31” shown at 918, indicating that thirty-one events having thevalue 920 “/Users/cburke/Desktop . . . /splunk/license_usage.log” thatis listed in the corresponding column and within the time increment 916.For a given row and given column, the aggregated number is the count ofthe field-value pairs that are within the designated time increment(also referred to as a “time bucket”). In implementations, theaggregated metrics 912 may represent any type of metric, such as acount, an average, or a sum of events, or any other aggregating metricassociated with a search result set of events. Alternatively or inaddition, the aggregated metrics 814 may represent an average number ofbytes downloaded, a sum of sales, or any other aggregated metric.

In implementations, a row 906 in the statistics time chart interface 900may be highlighted or otherwise emphasized when a pointer that isdisplayed moves over a particular row. This feature is also referred toas highlight with rollover (e.g., detected when a pointer moves over arow). For example, a user may move a computer mouse, stylus, or otherinput device pointer over a row 914, which is then displayed as anemphasized row. The emphasized row can then be selected in response to auser input, such as with a mouse click or touch input to select aparticular row, such as shown and described with reference to FIG. 9B.

The statistics time chart interface 900 can be displayed in a tableformat that includes one or more columns, each column having a columnheading comprising a different value, each different value associatedwith a particular event field. The statistics time chart interface 900also includes one or more rows, each row comprising a time increment andone or more aggregated metrics, each aggregated metric representing anumber of events having a field-value pair that matches the differentvalue represented in one of the columns and within the time incrementover which the aggregated metric is calculated. A row can be emphasizedthat includes the time increment and the one or more aggregated metricsin the emphasized row in the first interface, and in response, a menu isdisplayed with options that are selectable to transition to a secondinterface based on a selected one of the options. In embodiments, theoptions are selectable to transition to the second interface thatdisplays a listing of events that include the field-value pair thatmatches the different value represented in one of the columns and withinthe time increment of the emphasized row. Alternatively, a secondinterface is a statistics narrowed time interface for the time incrementin the emphasized row, the statistics narrowed time interface includingthe one or more columns each having the column heading comprising adifferent value associated with the particular event field as displayedin the first interface, and time metric rows, each comprising a narrowedtime metric of the time increment of the emphasized row in the firstinterface. The time metric rows can further comprise one or moreadditional aggregated metrics, each additional aggregated metricrepresenting a number of events having the field-value pair that matchesthe different value represented in one of the columns and within thenarrowed time metric.

FIG. 9B further illustrates the example of the statistics time chartinterface 900 described with reference to FIG. 9A in accordance with thedisclosed embodiments for statistics time chart interface row mode drilldown. In this example display of the statistics time chart interface900, a user has selected the emphasized row 906, such as with a mouseclick or touch input, which initiates display of a stats time range menu922 that is displayed responsive to the user input. In implementations,the stats time range menu 922 is displayed proximate the emphasized row906 in the statistics time chart interface 900, such as a pop-up ordrop-down menu just below the emphasized row.

The stats time range menu 922 includes options 924 that are selectableto transition to the search events interface 828 that is shown anddescribed with reference to FIG. 8C. For example, the options 924displayed in the stats time range menu 922 include an option “Viewevents” 926 that a user can select to transition to the search eventsinterface 828 that displays the list of the events that include afield-value pair with the respective value 928 that is listed in thecorresponding column 910 and within the time increment 930 of theemphasized row 906. A user can select the option 926 from the stats timerange menu 922 to drill down into events that match the token in theemphasized row and column, where the “token” is the field value 928 inthis example).

The options 924 displayed in the stats time range menu 922 also includean option “Narrow to this time range” 932 that a user can select totransition to a statistics narrowed time interface that is shown andfurther described with reference to FIG. 9C. The stats time range menu922 also includes a designation 934 of a time duration that encompassesthe time increment corresponding to the emphasized row. For example, thetime duration is designated as “04:00:00 to 04:30:00”, which encompassesthe time increment 930 and is a windowed 30-minutes of time. The option“Narrow to this time range” 932 can be selected to drill down and seethe events that fall into the time range between “04:00:00 to 04:30:00”,as shown in the statistics narrowed time interface in FIG. 9C.

FIG. 9C illustrates an example of a statistics narrowed time interface936 displayed as a graphical user interface in accordance with thedisclosed embodiments for statistics time chart interface row mode drilldown. The statistics narrowed time interface 936 includes the search bar902 that displays the search command 904, which indicates the eventfield “source”. The statistics narrowed time interface 936 includes theone or more columns 910 of the values associated with the event field(e.g., “source” in this example) as displayed in the statistics timechart interface 900. The statistics narrowed time interface 936 alsoincludes time metric rows 938 that each have a narrowed time metric 940of the time increment 908 of the emphasized row 906 in the statisticstime chart interface 900. In implementations, the narrowed time metrics940 of the time increment 908 can be incremented automatically or basedon user-defined increments, or may be incremented based on any form oflogical metrics, such as based on a number of events that are includedin a corresponding time metric (also referred to as a time “bucket”).Each of the time metric rows 938 in the statistics narrowed timeinterface 936 also include one or more additional aggregated metrics942, where each additional aggregated metric 942 identifies a number ofevents having the respective value that is listed in the correspondingcolumn and within the narrowed time metric.

Example Methods

Example methods 1000 and 1100 are described with reference to respectiveFIGS. 10 and 11 in accordance with one or more embodiments of statisticschart row mode drill down. Generally, any of the components, modules,methods, and operations described herein can be implemented usingsoftware, firmware, hardware (e.g., fixed logic circuitry), manualprocessing, or any combination thereof. Some operations of the examplemethods may be described in the general context of executableinstructions stored on computer-readable storage memory that is localand/or remote to a computer processing system, and implementations caninclude software applications, programs, functions, and the like.Alternatively or in addition, any of the functionality described hereincan be performed, at least in part, by one or more hardware logiccomponents, such as, and without limitation, Field-programmable GateArrays (FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SoCs), Complex Programmable Logic Devices (CPLDs), and the like.

Computing devices (to include server devices) can be implemented withvarious components, such as a processing system and memory, and with anynumber and combination of different components as further described withreference to the example device shown in FIG. 12 . One or more computingdevices can implement the search system, in hardware and at leastpartially in software, such as executable software instructions (e.g.,computer-executable instructions) that are executable with a processingsystem (e.g., one or more computer processors) implemented by the one ormore computing devices. The search system can be stored oncomputer-readable, non-volatile storage memory, such as any suitablememory device or electronic data storage implemented by the computingdevices.

FIG. 10 illustrates example method(s) 1000 of statistics value chartinterface row mode drill down, and is generally described with referenceto a statistics value chart interface. The method(s) 1000 may beimplemented by a computing device, a distributed system of computingdevices, and/or by one or more user client devices. The order in which amethod is described is not intended to be construed as a limitation, andany number or combination of the method operations and/or methods can beperformed in any order to implement a method, or an alternate method.

At 1002, a statistics value chart interface is displayed that includesone or more columns, each column comprising field values of an eventfield, and each column having a column heading comprising a differentone of the event fields, and the interface includes one or more rows,each row comprising one or more of the field values, each field value ina row associated with a different one of the event fields, and each rowcomprising an aggregated metric that represents a number of eventshaving field-value pairs that match all of the one or more field valueslisted in a respective row and the corresponding event fields listed inthe respective columns. For example, the statistics value chartinterface 800 (FIG. 8A) is displayed and includes the rows 806 of thefield values 808 for designated event fields 810. The statistics valuechart interface 800 also includes the aggregated metrics 814 that eachidentify the number of events having the field values 808 listed in arespective row 806.

At 1004, a row of the field values and the corresponding aggregatedmetric is emphasized in the statistics value chart interface. Forexample, a user may move a computer mouse, stylus, or other input devicepointer over a row 816, which is then displayed as an emphasized row(e.g., highlighted or any other type of visual emphasis). The emphasizedrow can then be selected in response to a user input, such as with amouse click or touch input to select a particular row.

At 1006, a selection of an aggregated metric in an emphasized row of thestatistics value chart interface is received and, at 1008, a stats eventmenu is displayed with event options that are selectable to transitionto a search events interface. For example, the aggregated metric 814 inthe emphasized row 816 can be selected as a user input, such as with amouse click or touch input to select the aggregated metric in theemphasized row. The stats event menu 818 (FIG. 8B) is then displayedwith the event options 820 responsive to the received user input, andthe stats event menu 818 is displayed proximate the emphasized row 816in the statistics value chart interface 800. The event options 820displayed in the stats event menu 818 include the option “View events”822 and the option “Other events” 824 that a user can select totransition to the search events interface 828 (FIG. 8C).

At 1010, a selection of the view events option is received to transitionto the search events interface that displays the listing of the eventsthat include the field-value pairs that match all of the field valueslisted in the emphasized row. For example, a user can select the option“View events” 822 in the stats event menu 818, and the search systemtransitions to the search events interface 828 that displays the listingof the events 830 that include the field values 808 listed in theemphasized row 816 of the statistics value chart interface 800.

At 1012, a selection of the other events option is received totransition to the search events interface that displays the listing ofthe other events that do not include the field-value pairs that matchall of the field values listed in the emphasized row. For example, auser can select the option “Other events” 824 in the stats event menu818, and the search system transitions to the search events interface828 that displays the listing of the other events 830 that do notinclude the field values 808 listed in the emphasized row 816 of thestatistics value chart interface 800.

FIG. 11 illustrates example method(s) 1100 of statistics time chartinterface row mode drill down, and is generally described with referenceto a statistics time chart interface. The method(s) 1100 may beimplemented by a computing device, a distributed system of computingdevices, and/or by one or more user client devices. The order in which amethod is described is not intended to be construed as a limitation, andany number or combination of the method operations and/or methods can beperformed in any order to implement a method, or an alternate method.

At 1102, a statistics time chart interface is displayed that includesone or more columns, each column having a column heading comprising adifferent value, each different value associated with a particular eventfield, and the interface includes one or more rows, each row comprisinga time increment and one or more aggregated metrics, each aggregatedmetric representing a number of events having a field-value pair thatmatches the different value represented in one of the columns and withinthe time increment over which the aggregated metric is calculated. Forexample, statistics time chart interface 900 (FIG. 9A) is displayed andincludes the rows 906 that are each designated by a time increment 908,and each time increment may include a date associated with the timeincrement. The statistics time chart interface 900 also includes thecolumns of values 910 that are associated with an event field. Each rowin the interface 900 includes a time increment 908 and one or moreaggregated metrics 912, where each aggregated metric represents a numberof the events having the respective value 910 that is listed in thecorresponding column and within the time increment.

At 1104, a row is emphasized that includes the time increment and theone or more aggregated metrics in the emphasized row in the statisticstime chart interface. For example, a user may move a computer mouse,stylus, or other input device pointer over any of the one or moreaggregated metrics 912 or the time increment 908 in a row 906, which isthen displayed as an emphasized row (e.g., highlighted or any other typeof visual emphasis). The emphasized row can then be selected in responseto a user input, such as with a mouse click or touch input to select aparticular row.

At 1106, a selection of an emphasized row in the statistics time chartinterface is received and, at 1108, a stats time range menu is displayedwith options that are selectable to transition to a search eventsinterface, or transition to a statistics narrowed time interface for thetime increment in the emphasized row. For example, the emphasized row906 can be selected as a user input, such as with a mouse click or touchinput to select the emphasized row. The stats time range menu 922 (FIG.9B) is then displayed proximate the emphasized row in the statisticstime chart interface 900 based on the selection of the emphasized row.The options displayed in the stats time range menu 922 include the viewevents option 926 and the narrow time range option 932. The stats timerange menu 922 includes the designation 934 of a time duration thatencompasses the time increment corresponding to the emphasized row.

At 1110, a selection of the view events option is received to transitionto the search events interface. For example, a user can select theoption “View events” 926 to transition to the search events interface828 (FIG. 8C) that displays a listing of events that include thefield-value pair that matches the different value represented in one ofthe columns and within the time increment of the emphasized row.

At 1112, a selection of the narrow time range option is received totransition to the statistics narrowed time interface. For example, auser can select the option “Narrow to this time range” 932 to transitionto the statistics narrowed time interface 936 (FIG. 9C) that includesthe one or more columns each having the column heading comprising adifferent value associated with the particular event field as displayedin the first interface, and time metric rows, each comprising a narrowedtime metric of the time increment of the emphasized row in the firstinterface. Each of the time metric rows 938 in the statistics narrowedtime interface 936 also include one or more additional aggregatedmetrics 942, where each additional aggregated metric 942 identifies anumber of events having the field-value pair that matches the differentvalue represented in one of the columns and within the narrowed timemetric.

Example System and Device

FIG. 12 illustrates an example system generally at 1200 that includes anexample computing device 1202 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe search interface module 1204 that is representative of functionalityto interact with a search service 1206, e.g., to specify and managesearches using a late-binding schema and events as described above andthus may correspond to the client application module 106 and system 100of FIG. 1 . The computing device 1202 may be, for example, a server of aservice provider, a device associated with a client (e.g., a clientdevice), an on-chip system, and/or any other suitable computing deviceor computing system.

The example computing device 1202 as illustrated includes a processingsystem 1208, one or more computer-readable media 1210, and one or moreI/O interface 1212 that are communicatively coupled, one to another.Although not shown, the computing device 1202 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 1208 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 1208 is illustrated as including hardware element 1214 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 1214 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 1210 is illustrated as includingmemory/storage 1216. The memory/storage 1216 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 1216 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 1216 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 1210 may be configured in a variety of otherways as further described below.

Input/output interface(s) 1212 are representative of functionality toallow a user to enter commands and information to computing device 1202,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 1202 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 1202. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 1202, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 1214 and computer-readablemedia 1210 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 1214. The computing device 1202 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device1202 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements1214 of the processing system 1208. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 1202 and/or processing systems1208) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 1202 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 1218 via a platform 1220 as describedbelow.

The cloud 1218 includes and/or is representative of a platform 1220 forresources 1222. The platform 1220 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 1218. Theresources 1222 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 1202. Resources 1222 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 1220 may abstract resources and functions to connect thecomputing device 1202 with other computing devices. The platform 1220may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources1222 that are implemented via the platform 1220. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 1200. Forexample, the functionality may be implemented in part on the computingdevice 1202 as well as via the platform 1220 that abstracts thefunctionality of the cloud 1218.

Although embodiments of statistics chart row mode drill down have beendescribed in language specific to features and/or methods, the appendedclaims are not necessarily limited to the specific features or methodsdescribed. Rather, the specific features and methods are disclosed asexample implementations of statistics chart row mode drill down, andother equivalent features and methods are intended to be within thescope of the appended claims. Further, various different embodiments aredescribed and it is to be appreciated that each described embodiment canbe implemented independently or in connection with one or more otherdescribed embodiments.

The invention claimed is:
 1. A computer-implemented method, comprising:causing display of a first interface, the first interface including atable, the table including a set of columns and a set of rows, wherein:each column of the set of columns corresponds to a particular value of afield-value pair for a first set of events, each row of the set of rowscorresponds to a particular time increment and includes one or moreaggregated metrics, each aggregated metric of a particular row of theset of rows corresponds to a particular column of the set of columns andindicates a number of events included in the first set of events thathave each occurred within the particular time increment of theparticular row and is associated with the particular value of theparticular column corresponding to the aggregated metric; in response toreceiving a first input that indicates a selection of a first row of theset of rows, causing display of a set of options that enable atransition to a second interface associated with the first row; and inresponse to receiving a second input that indicates a selection of afirst option of the set of options, causing display of the secondinterface.
 2. The computer-implemented method of claim 1, wherein theset of options further enables a selection of a second set of eventsbased on the first row and the second interface includes a listing ofthe second set of events.
 3. The computer-implemented method of claim 1,wherein the set of options is displayed within a menu of options that isdisplayed proximate the first row within the first interface.
 4. Thecomputer-implemented method of claim 1, wherein, based on the firstoption, the second interface includes a listing of a second set ofevents that is associated with the particular time increment of thefirst row.
 5. The computer-implemented method of claim 4, wherein thesecond set of events is further associated with at least one aggregatedmetric included in the first row.
 6. The computer-implemented method ofclaim 1, wherein first option enables a narrowing to the particular timeincrement of the first row.
 7. The computer-implemented method of claim1, wherein the second interface corresponds to the particular timeincrement of the first row, and comprises an other table, wherein eachrow of the other table corresponds to a respective sub range of theparticular time increment of the first row.
 8. A system comprising: oneor more processors; and a memory coupled to and accessible by the one ormore processors, the memory storing instructions that, when executed bythe one or more processors, cause the one or more processors to performoperations including: causing display of a first interface, the firstinterface including a table, the table including a set of columns and aset of rows, wherein: each column of the set of columns corresponds to aparticular value of a field-value pair for a first set of events, eachrow of the set of rows corresponds to a particular time increment andincludes one or more aggregated metrics, each aggregated metric of aparticular row of the set of rows corresponds to a particular column ofthe set of columns and indicates a number of events included in thefirst set of events that have each occurred within the particular timeincrement of the particular row and is associated with the particularvalue of the particular column corresponding to the aggregated metric;in response to receiving a first input that indicates a selection of afirst row of the set of rows, causing display of a set of options thatenable a transition to a second interface associated with the first row;and in response to receiving a second input that indicates a selectionof a first option of the set of options, causing display of the secondinterface.
 9. The system of claim 8, wherein the set of options furtherenables a selection of a second set of events based on the first row andthe second interface includes a listing of the second set of events. 10.The system of claim 8, wherein the set of options is displayed within amenu of options that is displayed proximate the first row within thefirst interface.
 11. The system of claim 8, wherein, based on the firstoption, the second interface includes a listing of a second set ofevents that is associated with the particular time increment of thefirst row.
 12. The system of claim 11, wherein the second set of eventsis further associated with at least one aggregated metric included inthe first row.
 13. The system of claim 8, wherein first option enables anarrowing to the particular time increment of the first row.
 14. Thesystem of claim 8, wherein the second interface corresponds to theparticular time increment of the first row, and comprises another table,wherein each row of the other table corresponds to a respective subrange of the particular time increment of the first row.
 15. Anon-transitory computer-readable storage memory having stored thereoninstructions that, when executed by one or more processors, cause theone or more processors to perform operations including: causing displayof a first interface, the first interface including a table, the tableincluding a set of columns and a set of rows, wherein: each column ofthe set of columns corresponds to a particular value of a field-valuepair for a first set of events, each row of the set of rows correspondsto a particular time increment and includes one or more aggregatedmetrics, each aggregated metric of a particular row of the set of rowscorresponds to a particular column of the set of columns and indicates anumber of events included in the first set of events that have eachoccurred within the particular time increment of the particular row andis associated with the particular value of the particular columncorresponding to the aggregated metric; in response to receiving a firstinput that indicates a selection of a first row of the set of rows,causing display of a set of options that enable a transition to a secondinterface associated with the first row; and in response to receiving asecond input that indicates a selection of a first option of the set ofoptions, causing display of the second interface.
 16. The non-transitorycomputer-readable storage memory of claim 15, wherein the set of optionsfurther enables a selection of a second set of events based on the firstrow and the second interface includes a listing of the second set ofevents.
 17. The non-transitory computer-readable storage memory of claim15, wherein the set of options is displayed within a menu of optionsthat is displayed proximate the first row within the first interface.18. The non-transitory computer-readable storage memory of claim 15,wherein, based on the first option, the second interface includes alisting of a second set of events that is associated with the particulartime increment of the first row.
 19. The non-transitorycomputer-readable storage memory of claim 18, wherein the second set ofevents is further associated with at least one aggregated metricincluded in the first row.
 20. The non-transitory computer-readablestorage memory of claim 15, wherein the second interface corresponds tothe particular time increment of the first row, and comprises an othertable, wherein each row of the other table corresponds to a respectivesub range of the particular time increment of the first row.